Prognostic Significance of the Surprise Question in Cancer Patients

Moss, Alvin H.; Lunney, June R.; Culp, Stacey; Auber, Miklos; Kurian, Sobha; Rogers, John; Dower, Joshua; Abraham, Jame

doi:10.1016/j.jpainsymman.2009.11.265

Cited by 40 publications

(46 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[33][34][35] More limited literature demonstrates that adding outpatient utilization information and psychosocial factors improve predictive models for acute care utilization. [35][36][37] Prior studies of physician's ability to predict death or acute care utilization results show mixed results; [13][14][15][16][17][18]22 however, our quantitative model, designed to predict physician-defined complexity from increasingly available data, shows promise as an additional tool in identifying and stratifying high-risk patients for population management interventions. Health care delivery systems could replicate our approach in their own context or add proxies for psychosocial complexity that are available in their data repositories to strengthen their risk-stratification approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Current quantitative methods for identifying the complex patients at highest risk for suboptimal future clinical quality and utilization outcomes rely primarily on diagnosis-based and utilization-based algorithms to predict future utilization. [13][14][15][16][17][18][19][20][21][22][23] These tools miss clinical characteristics that are not present in billing data and may not capture non-clinical contributors to patient complexity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

et al. 2015

View full text Add to dashboard Cite

BACKGROUND: Improving the ability to risk-stratify patients is critical for efficiently allocating resources within healthcare systems. OBJECTIVE: The purpose of this study was to evaluate a physician-defined complexity prediction model against outpatient Charlson score (OCS) and a commercial risk predictor (CRP). DESIGN: Using a cohort in which primary care physicians reviewed 4302 of their adult patients, we developed a predictive model for estimated physician-defined complexity (ePDC) and categorized our population using ePDC, OCS and CRP. PARTICIPANTS: 143,372 primary care patients in a practice-based research network participated in the study. MAIN MEASURES: For all patients categorized as complex in 2007 by one or more risk-stratification method, we calculated the percentage of total person time from 2008-2011 for which eligible cancer screening was incomplete, HbA1c was ≥ 9 %, and LDL was ≥ 130 mg/dl (in patients with cardiovascular disease). We also calculated the number of emergency department (ED) visits and hospital admissions per person year (ppy). KEY RESULTS: There was modest agreement among individuals classified as complex using ePDC compared with OCS (36.7 %) and CRP (39.6 %). Over 4 follow-up years, eligible ePDC-complex patients had higher proportions (p<0.001) of time with: incomplete cervical (17.8 % vs. 13.3 % for OCS; 19.4 % vs. 11.2 % for CRP), breast (21.4 % vs. 14.9 % for OCS; 22.7 % vs. 15.0 % for CRP), and colon (25.9 % vs. 18.7 % for OCS; 27.0 % vs. 18.2 % for CRP) cancer screening; HbA1c≥9 % (15.6 % vs. 8.1 % for OCS; 15.9 % vs. 6.9 % for CRP); and LDL≥130 mg/dl (12.4 % vs. 7.9 % for OCS; 11.8 % vs 9.0 % for CRP). ePDCcomplex patients had higher rates (p<0.003) of: ED visits (0.21 vs. 0.11 ppy for OCS; 0.17 vs. 0.15 ppy for CRP), and admissions in patients 45-64 and ≥ 65 years old (0.11 vs. 0.10 ppy AND 0.24 vs. 0.21 ppy for OCS). CONCLUSION: Our measure for estimated physiciandefined complexity compared favorably to commonly used risk-prediction approaches in identifying future suboptimal quality and utilization outcomes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Some researchers have proposed the "surprise question," in which clinicians were asked "Would I be surprised if this patient died in 1 year?" This concept resulted in an accuracy rate of 88%, with a positive predictive value of 41% and negative predictive value of 97% [18]. A challenge with this approach is that it forces a binary response with a "yes" or "no" answer.…”

Section: Discussionmentioning

confidence: 99%

The Accuracy of Probabilistic Versus Temporal Clinician Prediction of Survival for Patients with Advanced Cancer: A Preliminary Report

et al. 2011

View full text Add to dashboard Cite

Clinicians have limited accuracy in the prediction of patient survival. We assessed the accuracy of probabilistic clinician prediction of survival (CPS) and temporal CPS for advanced cancer patients admitted to our acute palliative care unit, and identified factors associated with CPS accuracy. Eight physicians and 20 nurses provided their estimation of survival on admission by (a) the temporal approach, "What is the approximate survival for this patient (in days)?" and (b) the probabilistic approach, "What is the approximate probability that this patient will be alive (0%-100%)?" for >24 hours, 48 hours, 1 week, 2 weeks, 1 month, 3 months, and 6 months. We also collected patient and clinician demographics. Among 151 patients, the median age was 58 years, 95 (63%) were female, and 138 (81%) had solid tumors. The median overall survival time was 12 days. The median temporal CPS was 14 days for physicians and 20 days for nurses. Physicians were more accurate than nurses. A higher accuracy of temporal physician CPS was associated with older patient age. Probabilistic CPS was significantly more accurate than temporal CPS for both physicians and nurses, although this analysis was limited by the different criteria for determining accuracy. With the probabilistic approach, nurses were significantly more accurate at predicting survival at 24 hours and 48 hours, whereas physicians were significantly more accurate at predicting survival at 6 months. The probabilistic approach was associated with high accuracy and has practical implications. The Oncologist 2011;16:1642-1648

show abstract

“…''), which has been found to be a simple, feasible, and effective tool to identify patients with cancer who have a greatly increased risk of one-year mortality and would be appropriate for palliative care. 10 Up to 71.0% fellows responded that they did have resources such as counseling services offered to help them manage their own feelings about a patient's death and 21.7% of respondents felt that the role of spirituality was ''not important. ''…”

Section: Associations Between Respondent Characteristics and Dependenmentioning

confidence: 99%

Palliative Care Training during Fellowship: A National Survey of U.S. Hematology and Oncology Fellows

Thomas

Curley

Wen

et al. 2015

Journal of Palliative Medicine

View full text Add to dashboard Cite

Background: Despite requirements for palliative care training during fellowship, there is a paucity of recent data regarding the attitudes, knowledge, and skills of hematology/ oncology fellows in palliative care. Objective: Our aim was to assess fellows' attitudes toward and quality of training in palliative care during fellowship and perceived preparedness to care for patients at the end of life (EOL). Methods: In May 2013 a cross-sectional survey of hematology/oncology fellows was conducted. Results: Fellows from 93 of 138 fellowship programs responded (67.4%). Of the 347 fellows e-mailed, 176 participated. Nearly all fellows (99%) indicated that physicians have a responsibility to help patients at EOL. Fellows felt their overall training in fellowship was superior to training in EOL care (4.24 -0.78 versus 3.53 -0.99 on a 5-5 scale where 1 = poor and 5 = excellent, p < 0.0001). Fellows who had a rotation in palliative care during fellowship (44.9%) reported better teaching on managing a patient at EOL than those who did not (3.91 -1.0 versus 3.21 -0.87, p < 0.0001). Fellows reporting better teaching in EOL care felt better prepared to care for patients at EOL (r = 0.52, p < 0.0001). More than 25% reported not being explicitly taught how to assess prognosis, when to refer a patient to hospice, or how to conduct a family meeting to discuss treatment options. Conclusion: Many recent oncology fellows are still inadequately prepared to provide palliative care to their patients. There is significant room for improvement with regards to the quality of palliative care training in U.S. hematology/oncology fellowship programs.

show abstract

Prognostic Significance of the Surprise Question in Cancer Patients

Abstract: Objectives 1. Discuss the use of the surprise question. 2. Describe outcomes with the use of the surprise question in cancer patient care.

Cited by 40 publications

References 10 publications

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network

The Accuracy of Probabilistic Versus Temporal Clinician Prediction of Survival for Patients with Advanced Cancer: A Preliminary Report

Palliative Care Training during Fellowship: A National Survey of U.S. Hematology and Oncology Fellows

Contact Info

Product

Resources

About